CN117297593B - Action detection system and detection method for ankylosing spondylitis patient - Google Patents
Action detection system and detection method for ankylosing spondylitis patient Download PDFInfo
- Publication number
- CN117297593B CN117297593B CN202311411766.4A CN202311411766A CN117297593B CN 117297593 B CN117297593 B CN 117297593B CN 202311411766 A CN202311411766 A CN 202311411766A CN 117297593 B CN117297593 B CN 117297593B
- Authority
- CN
- China
- Prior art keywords
- value
- deviation
- data
- module
- jpy
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 51
- 230000009471 action Effects 0.000 title claims abstract description 43
- 206010002556 Ankylosing Spondylitis Diseases 0.000 title claims abstract description 25
- 238000000034 method Methods 0.000 claims abstract description 84
- 230000008569 process Effects 0.000 claims abstract description 63
- 238000006073 displacement reaction Methods 0.000 claims abstract description 31
- 230000003068 static effect Effects 0.000 claims abstract description 22
- 238000004458 analytical method Methods 0.000 claims abstract description 13
- 238000007405 data analysis Methods 0.000 claims description 12
- 238000010276 construction Methods 0.000 claims description 9
- 230000000694 effects Effects 0.000 claims description 4
- 238000000926 separation method Methods 0.000 claims description 3
- 230000006641 stabilisation Effects 0.000 claims description 3
- 238000011105 stabilization Methods 0.000 claims description 3
- 230000001225 therapeutic effect Effects 0.000 claims description 3
- 238000003745 diagnosis Methods 0.000 abstract description 3
- 208000024891 symptom Diseases 0.000 abstract description 2
- 210000002414 leg Anatomy 0.000 description 8
- 210000000988 bone and bone Anatomy 0.000 description 5
- 230000008859 change Effects 0.000 description 5
- 238000003384 imaging method Methods 0.000 description 3
- 238000002591 computed tomography Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000002372 labelling Methods 0.000 description 2
- 238000002595 magnetic resonance imaging Methods 0.000 description 2
- 210000003205 muscle Anatomy 0.000 description 2
- 206010051728 Bone erosion Diseases 0.000 description 1
- 206010061218 Inflammation Diseases 0.000 description 1
- 208000031481 Pathologic Constriction Diseases 0.000 description 1
- 230000004888 barrier function Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000001886 ciliary effect Effects 0.000 description 1
- 230000006378 damage Effects 0.000 description 1
- 230000005786 degenerative changes Effects 0.000 description 1
- 201000010099 disease Diseases 0.000 description 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
- 230000004054 inflammatory process Effects 0.000 description 1
- 230000002427 irreversible effect Effects 0.000 description 1
- 210000000629 knee joint Anatomy 0.000 description 1
- 230000000670 limiting effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000004660 morphological change Effects 0.000 description 1
- 208000015122 neurodegenerative disease Diseases 0.000 description 1
- 230000008506 pathogenesis Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 230000002829 reductive effect Effects 0.000 description 1
- 230000036262 stenosis Effects 0.000 description 1
- 208000037804 stenosis Diseases 0.000 description 1
- 238000002604 ultrasonography Methods 0.000 description 1
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/1118—Determining activity level
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/1123—Discriminating type of movement, e.g. walking or running
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/45—For evaluating or diagnosing the musculoskeletal system or teeth
- A61B5/4538—Evaluating a particular part of the muscoloskeletal system or a particular medical condition
- A61B5/4566—Evaluating the spine
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
Abstract
The invention discloses a motion detection system for ankylosing spondylitis patients, which comprises a data acquisition module: for obtaining static data values, dynamic data values, action curve data values and historical data values for the patient; and a linear fitting module: the system comprises a point processing unit, a linear processing unit and a surface processing unit, which are respectively used for processing the data value acquired by the data acquisition module; and an analysis and judgment module: the method comprises the steps of performing process calculation on a deviation value, a displacement value and a dynamic jump value to obtain a process deviation superposition value; respectively comparing the process deviation overlapping value with a deviation threshold value, and generating a detection signal according to a comparison result; and a linear display module: and the device is used for transmitting the static data value, the dynamic data value, the action curve data value and the detection signal to the display end. The diagnosis and detection can be carried out on the ankylosing spondylitis patient from multiple aspects, the data of the symptoms are presented, a better judgment reference basis is provided for doctors, and the timely and effective intervention or treatment of the patient is ensured.
Description
Technical Field
The invention relates to the technical field of action detection, in particular to an action detection system and method for ankylosing spondylitis patients.
Background
Ankylosing Spondylitis (AS) includes a group of interrelated diseases characterized by inflammation of the bones and joints and the spinal column, peripheral joints and points of attachment of the muscle ciliary muscle, and the like, and manifests itself mainly in narrowing of the bone gap. The etiology of AS is complex, pathogenesis is not clear, early clinical manifestations of patients are atypical, and lack of specific laboratory indexes and the like all cause great barriers for clinicians to accurately judge AS in early stage, so that serious consequences are often caused when diagnosis and treatment are not in time, irreversible bone destruction is brought to patients, and even lifelong disability is caused.
Currently, the imaging examination methods commonly used in clinic include X-ray, computer Tomography (CT), magnetic Resonance Imaging (MRI), ultrasound, and radionuclide bone imaging. Although MRI is a superior technique in showing small areas of degenerative changes, currently conventional X-ray films remain the primary examination for detecting degenerative diseases of the plasma and knee joints. The most common clinical examination method is still X-ray, and the imaging examination result can show the structural morphological changes of the bone, such as bone erosion, hardening, joint rigidity, joint gap widening or stenosis.
However, the doctor only judges through the X-ray image, and can not judge the tiny change of the body part of the patient very accurately, and meanwhile, the image can not be visually presented to the patient and the doctor, and no accurate data basis exists in the aspects of intervention or treatment selection after detection.
Disclosure of Invention
The invention aims to provide a motion detection system and a motion detection method for ankylosing spondylitis patients, which are used for solving the technical problems in the background.
The aim of the invention can be achieved by the following technical scheme:
a motion detection system for a ankylosing spondylitis patient, comprising a data acquisition module: for acquiring static data values, dynamic data values and action curve data values for the patient; the method comprises the steps of obtaining a historical data value in a server;
and a linear fitting module: the system comprises a point processing unit, a linear processing unit and a surface processing unit, which are respectively used for processing the data value acquired by the data acquisition module;
and an analysis and judgment module: the method comprises the steps of performing process calculation on a deviation value, a displacement value and a dynamic jump value to obtain a process deviation superposition value; respectively comparing the process deviation overlapping value with a deviation threshold value, and generating a detection signal according to a comparison result;
and a linear display module: the method comprises the steps of carrying out line-face construction on a static data value, a dynamic data value and an action curve data value, and sending the line-face construction to a display end, and obtaining a detection signal and sending the detection signal to the display end.
As a further scheme of the invention: the working steps of the data acquisition module comprise:
establishing a virtual coordinate system by using a behavior detection room;
respectively acquiring body position values Tw of all body positions of a patient under static state through a body sensor;
the maximum posture value Tt of each posture of the patient in a standing state is respectively obtained through a human body sensor;
respectively acquiring action curve data values Tq of all positions of a patient in the walking process through a human body sensor;
the body position comprises a left arm position, a right arm position, a left leg position, a right leg position and a neck position;
and respectively transmitting the body position value, the maximum body state value and the curve data value to a data analysis module.
As a further scheme of the invention: the point processing unit is used for processing the acquired body position value Tw and marking: comprising the following steps: the body position values Tw of the left arm position, the right arm position, the left leg position, the right leg position and the neck position are respectively marked as Zb (x, y), yb (x, y), zt (x, y), yt (x, y) and Bj (x, y);
acquiring a body position value TWL in the historical data value; by passing throughCalculating to obtain a single deviation value Pd of each body position;
then pass throughCalculating to obtain a body position deviation value P;
the deviation value P is sent to a data analysis module.
As a further scheme of the invention: the linear processing unit is configured to process the obtained maximum state value Tt and perform labeling: obtaining a maximum posture value of each posture comprises the following steps: zbmax (x, y), ybmax (x, y), ztmax (x, y), ytmax (x, y), and Bjmax (x, y);
space coordinate construction is carried out on the maximum attitude value Tt and the attitude value Tw and the maximum attitude value Tt and the attitude value Tw are passed throughCalculating to obtain a displacement single value Wd of each body position, and marking to obtain the displacement single value of each body position, wherein the displacement single value comprises Wdzb, wdyb, wdzt, wdyt and Wdbj;
according toCalculating to obtain a displacement value W;
and sending the displacement value W to a data analysis module.
As a further scheme of the invention: the surface processing unit is used for processing the obtained action curve data value Tq, constructing a curve data graph in the action process through space coordinates, marking the action curve data value Tq in each period by taking one walking period as a separation bit, and marking the action curve data value Tq as Tqzb (i), tqyb (i), tqzt (i), tqyt (i) and Tqbj (i) respectively, wherein i is a positive integer greater than zero;
acquiring action curve data values Tq in adjacent periods, denoted Tqm (i) and Tqn (i), respectively, byAcquiring a dynamic jump single value Fd of each body position; and marking to obtain dynamic jump single values of all the body positions, including Fdzb, fdyb, fdzt, fdyt and Fdbj.
According toCalculating to obtain a dynamic jump value F of each body position;
and sending the dynamic jump value F to a data analysis module.
As a further scheme of the invention: the process deviation overlap values include a primary process deviation overlap value Jpy and a secondary process deviation overlap value Jpe, and the deviation thresholds include a primary deviation threshold Jpy0 and a secondary deviation threshold Jpe0.
As a further scheme of the invention: the calculation mode of the first-stage process deviation superposition value Jpy is as follows:
wherein->And alpha > 0, beta > 0.
The calculation mode of the secondary process deviation superposition value Jpe is as follows:
wherein->And γ > 0, μ > 0.
As a further scheme of the invention: the analysis and judgment module comprises the following specific steps:
step A1: performing process calculation on the deviation value, the displacement value and the dynamic jump value to obtain a primary process deviation superposition value Jpy and a secondary process deviation superposition value Jpe;
step A2: a first-order deviation threshold Jpy and a second-order deviation threshold Jpe0 of the deviation thresholds are obtained;
step A3: comparing the first-level process deviation superposition value Jpy with a first-level deviation threshold Jpy 0;
if Jpy is less than or equal to Jpy, generating a stable signal;
if Jpy is more than Jpy, generating an early warning signal; then executing the step A4;
step A4: comparing the secondary process deviation overlap value Jpe with a secondary deviation threshold Jpe0;
if Jpe is less than or equal to Jpe, generating an intervention signal;
if Jpe > Jpe, generating a therapeutic signal;
and sends the stabilization signal, the intervention signal, the treatment signal, etc. to the linear display module.
As a further scheme of the invention: the linear display module comprises the following specific steps:
acquiring a static data value, a dynamic data value and an action curve data value and combining the virtual coordinate system to display on a display end;
and sending the corresponding detection signals to a display end for display according to the judging result of the analysis judging module.
As a further scheme of the invention: a method of detecting movement in a patient suffering from ankylosing spondylitis comprising the steps of:
the method comprises the steps that firstly, body position values, maximum body state values and action curve data values of all body positions of a patient under static state are respectively obtained through a body sensor and sent to a data analysis module and a linear display module;
step two: the obtained data are processed through a point processing unit, a linear processing unit and a surface processing unit in the linear fitting module to obtain a deviation value, a displacement value and a dynamic jump value, and the deviation value, the displacement value and the dynamic jump value are sent to the analysis and judgment module;
step three: performing process calculation through the deviation value, the displacement value and the dynamic jump value to obtain a primary process deviation superposition value Jpy and a secondary process deviation superposition value Jpe; the first-level process deviation superposition value Jpy and the second-level process deviation superposition value Jpe are respectively compared with a first-level deviation threshold Jpy and a second-level deviation threshold Jpe0, and detection signals are generated;
step four: and displaying the body position value, the maximum attitude value and the action curve data value and the detection signal through the linear display module.
The invention has the beneficial effects that:
(1) According to the invention, through the three-dimensional space coordinate drawing of the basic body position of the patient and the combination of the history data, the body change condition during the detection period can be effectively obtained, meanwhile, the standing and walking conditions of all parts of the portable body are detected by the three-dimensional space data values, the body change of the patient can be intuitively and effectively seen, the parts of the body of the patient with weight or reduced are better judged, and the detection effect is better;
(2) By detecting the static standing and walking movement states of the patient, the static data and the dynamic data are fitted with each other, the physical condition of the patient is judged, whether the patient needs intervention and treatment or not is accurately obtained, the data are intuitively displayed on one hand, and the doctor is better in reference value on the other hand.
Drawings
The invention is further described below with reference to the accompanying drawings.
FIG. 1 is a block diagram of a system of the present invention;
FIG. 2 is a schematic diagram of the workflow of the analysis decision module of the present invention;
FIG. 3 is a schematic flow chart of the detection method in the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1, the present invention is a motion detection system for ankylosing spondylitis patients, comprising a data acquisition module: for acquiring static data values, dynamic data values and action curve data values for the patient; the method comprises the steps of obtaining a historical data value in a server;
and a linear fitting module: the system comprises a point processing unit, a linear processing unit and a surface processing unit, which are respectively used for processing the data value acquired by the data acquisition module;
and an analysis and judgment module: the method comprises the steps of performing process calculation on a deviation value, a displacement value and a dynamic jump value to obtain a process deviation superposition value; respectively comparing the process deviation overlapping value with a deviation threshold value, and generating a detection signal according to a comparison result;
and a linear display module: the method comprises the steps of carrying out line-face construction on a static data value, a dynamic data value and an action curve data value, and sending the line-face construction to a display end, and obtaining a detection signal and sending the detection signal to the display end.
The diagnosis and detection can be carried out on the ankylosing spondylitis patient from multiple aspects, the data of the symptoms are presented, a better judgment reference basis is provided for doctors, and the timely and effective intervention or treatment of the patient is ensured.
The working steps of the data acquisition module are as follows: establishing a virtual coordinate system by using a behavior detection room;
respectively acquiring body position values Tw of all body positions of a patient under static state through a body sensor;
the maximum posture value Tt of each posture of the patient in a standing state is respectively obtained through a human body sensor;
respectively acquiring action curve data values Tq of all positions of a patient in the walking process through a human body sensor;
the body position comprises a left arm position, a right arm position, a left leg position, a right leg position and a neck position;
and respectively transmitting the body position value, the maximum body state value and the curve data value to a data analysis module.
In addition, the specific steps of the linear display module are as follows:
acquiring a static data value, a dynamic data value and an action curve data value and combining the virtual coordinate system to display on a display end;
and sending the corresponding detection signals to a display end for display according to the judging result of the analysis judging module.
Based on the above embodiments, the present application provides the following embodiments:
example two
In the linear fitting module:
the point processing unit is used for processing the acquired body position value Tw and marking: comprising the following steps: the body position values Tw of the left arm position, the right arm position, the left leg position, the right leg position and the neck position are respectively marked as Zb (x, y), yb (x, y), zt (x, y), yt (x, y) and Bj (x, y);
acquiring a body position value TWL in the historical data value; by passing throughCalculating to obtain a single deviation value Pd of each body position;
for example: the left arm bit is Zb (x, y), the historical data value is Zb (x 0, y 0), the deviation value single value Pd is ；
Then pass throughCalculating to obtain a body position deviation value P;
the linear processing unit is configured to process the obtained maximum state value Tt and perform labeling: obtaining a maximum posture value of each posture comprises the following steps: zbmax (x, y), ybmax (x, y), ztmax (x, y), ytmax (x, y), and Bjmax (x, y);
space coordinate construction is carried out on the maximum attitude value Tt and the attitude value Tw and the maximum attitude value Tt and the attitude value Tw are passed throughCalculating to obtain a displacement single value Wd of each body position, and marking to obtain the displacement single value of each body position, wherein the displacement single value comprises Wdzb, wdyb, wdzt, wdyt and Wdbj;
for example, the static body position value bit Zb (x, y) of the left arm bit, the maximum body state value is Zbmax (x 1, y 1), the displacement unit value Wd is ；
According toCalculating to obtain a displacement value W;
the surface processing unit is used for processing the acquired action curve data value Tq, constructing a curve data graph in the action process through space coordinates, marking the action curve data value Tq of the action curve in each period by taking one walking period as a separation bit, and marking the action curve data value Tq as Tqzb (i), tqyb (i), tqzt (i), tqyt (i) and Tqbj (i) respectively, wherein i is a positive integer greater than zero;
acquiring action curve data values Tq in adjacent periods, denoted Tqm (i) and Tqn (i), respectively, byAcquiring a dynamic jump single value Fd of each body position; and marking to obtain dynamic jump single values of all the body positions, including Fdzb, fdyb, fdzt, fdyt and Fdbj.
According toCalculating to obtain a dynamic jump value F of each body position;
through the three-dimensional space coordinate of the basic body position of the patient, the body change condition during the detection period can be effectively obtained by combining the historical data, meanwhile, all parts of the portable body are detected by three-dimensional space data values under the standing and walking conditions, the body change of the patient can be intuitively and effectively seen, the parts of the patient with heavy or light body can be better judged, and the detection effect is better.
Based on the above second embodiment, the present application discloses the following embodiments:
example III
The process deviation overlap values include a primary process deviation overlap value Jpy and a secondary process deviation overlap value Jpe, and the deviation thresholds include a primary deviation threshold Jpy0 and a secondary deviation threshold Jpe0.
By passing throughWherein->Alpha is more than 0, beta is more than 0, and a first-stage process deviation superposition value Jpy is obtained through calculation;
wherein->And gamma is more than 0, mu is more than 0, and a secondary process deviation superposition value Jpe is obtained through calculation;
the following steps are then performed:
step A1: performing process calculation on the deviation value, the displacement value and the dynamic jump value to obtain a primary process deviation superposition value Jpy and a secondary process deviation superposition value Jpe;
step A2: a first-order deviation threshold Jpy and a second-order deviation threshold Jpe0 of the deviation thresholds are obtained;
step A3: comparing the first-level process deviation superposition value Jpy with a first-level deviation threshold Jpy 0;
if Jpy is less than or equal to Jpy, generating a stable signal;
if Jpy is more than Jpy, generating an early warning signal; then executing the step A4;
step A4: comparing the secondary process deviation overlap value Jpe with a secondary deviation threshold Jpe0;
if Jpe is less than or equal to Jpe, generating an intervention signal;
if Jpe > Jpe, generating a therapeutic signal;
and sends the stabilization signal, the intervention signal, the treatment signal, etc. to the linear display module.
By detecting the static standing and walking movement states of the patient, the static data and the dynamic data are fitted with each other, the physical condition of the patient is judged, whether the patient needs intervention and treatment or not is accurately obtained, the data are intuitively displayed on one hand, and the doctor is better in reference value on the other hand.
Based on the above system, the present application provides a method for detecting actions of a patient suffering from ankylosing spondylitis, comprising the steps of:
the method comprises the steps that firstly, body position values, maximum body state values and action curve data values of all body positions of a patient under static state are respectively obtained through a body sensor and sent to a data analysis module and a linear display module;
step two: the obtained data are processed through a point processing unit, a linear processing unit and a surface processing unit in the linear fitting module to obtain a deviation value, a displacement value and a dynamic jump value, and the deviation value, the displacement value and the dynamic jump value are sent to the analysis and judgment module;
step three: performing process calculation through the deviation value, the displacement value and the dynamic jump value to obtain a primary process deviation superposition value Jpy and a secondary process deviation superposition value Jpe; the first-level process deviation superposition value Jpy and the second-level process deviation superposition value Jpe are respectively compared with a first-level deviation threshold Jpy and a second-level deviation threshold Jpe0, and detection signals are generated;
step four: and displaying the body position value, the maximum attitude value and the action curve data value and the detection signal through the linear display module.
The foregoing describes one embodiment of the present invention in detail, but the description is only a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention. All equivalent changes and modifications within the scope of the present invention are intended to be covered by the present invention.
Claims (10)
1. A motion detection system for a ankylosing spondylitis patient, comprising a data acquisition module: for acquiring static data values, dynamic data values and action curve data values for the patient; the method comprises the steps of obtaining a historical data value in a server;
and a linear fitting module: the system comprises a point processing unit, a linear processing unit and a surface processing unit, which are respectively used for processing the data value acquired by the data acquisition module;
and an analysis and judgment module: the method comprises the steps of performing process calculation on a deviation value, a displacement value and a dynamic jump value to obtain a process deviation superposition value; respectively comparing the process deviation overlapping value with a deviation threshold value, and generating a detection signal according to a comparison result;
and a linear display module: the method comprises the steps of carrying out line-face construction on a static data value, a dynamic data value and an action curve data value, and sending the line-face construction to a display end, and obtaining a detection signal and sending the detection signal to the display end.
2. The motion detection system for ankylosing spondylitis patients according to claim 1, wherein the working step of the data acquisition module comprises:
establishing a virtual coordinate system by using a behavior detection room;
respectively acquiring body position values Tw of all body positions of a patient under static state through a body sensor;
the maximum posture value Tt of each posture of the patient in a standing state is respectively obtained through a human body sensor;
respectively acquiring action curve data values Tq of all positions of a patient in the walking process through a human body sensor;
the body position comprises a left arm position, a right arm position, a left leg position, a right leg position and a neck position;
and respectively transmitting the body position value, the maximum body state value and the curve data value to a data analysis module.
3. The motion detection system for ankylosing spondylitis patients according to claim 1, wherein the point processing unit is configured to process the acquired body position value Tw and perform marking: comprising the following steps: the body position values Tw of the left arm position, the right arm position, the left leg position, the right leg position and the neck position are respectively marked as Zb (x, y), yb (x, y), zt (x, y), yt (x, y) and Bj (x, y);
acquiring a body position value TWL in the historical data value; calculating to obtain a single deviation value Pd of each body position through Pd=Tw-Twl;
then pass throughCalculating to obtain a body position deviation value P;
the deviation value P is sent to a data analysis module.
4. The activity detection system for ankylosing spondylitis patients according to claim 1, characterized in that the linear processing unit is configured to process the obtained maximum posture value Tt and to perform marking: obtaining a maximum posture value of each posture comprises the following steps: zbmax (x, y), ybmax (x, y), ztmax (x, y), ytmax (x, y), and Bjmax (x, y);
carrying out space coordinate construction on the maximum posture value Tt and the posture value Tw, calculating to obtain a displacement single value Wd of each posture through Wd=Tt-Tw, and marking to obtain the displacement single value of each posture, wherein the displacement single value comprises Wdzb, wdyb, wdzt, wdyt and Wdbj;
according toCalculating to obtain a displacement value W;
and sending the displacement value W to a data analysis module.
5. The action detection system for ankylosing spondylitis patients according to claim 1, wherein the surface processing unit is configured to process the acquired action curve data values Tq, and then construct a curve data map during the action by using spatial coordinates and mark the action curve in each cycle with one walking cycle as a separation bit, and the action curve data values Tq are marked as Tqzb (i), tqyb (i), tqzt (i), tqyt (i), and Tqbj (i), respectively, where i is a positive integer greater than zero;
acquiring action curve data values Tq in adjacent periods, denoted Tqm (i) and Tqn (i), respectively, byAcquiring a dynamic jump single value Fd of each body position; marking to obtain dynamic jump single values of all the body positions, including Fdzb, fdyb, fdzt, fdyt and Fdbj;
according toCalculating to obtain a dynamic jump value F of each body position;
and sending the dynamic jump value F to a data analysis module.
6. The activity detection system for a ankylosing spondylitis patient of claim 1, wherein the course bias stack values include a first course bias stack value Jpy and a second course bias stack value Jpe, and the bias thresholds include a first bias threshold Jpy0 and a second bias threshold Jpe0.
7. The motion detection system for ankylosing spondylitis patients according to claim 6, wherein the first order course deviation stacking value Jpy is calculated by:
wherein α+β=1, and α > 0, β > 0;
the calculation mode of the secondary process deviation superposition value Jpe is as follows:
wherein γ+μ=1, and γ > 0, μ > 0.
8. The motion detection system for ankylosing spondylitis patients according to claim 1, wherein the specific steps of the analysis and determination module are:
step A1: performing process calculation on the deviation value, the displacement value and the dynamic jump value to obtain a primary process deviation superposition value Jpy and a secondary process deviation superposition value Jpe;
step A2: a first-order deviation threshold Jpy and a second-order deviation threshold Jpe0 of the deviation thresholds are obtained;
step A3: comparing the first-level process deviation superposition value Jpy with a first-level deviation threshold Jpy 0;
if Jpy is less than or equal to Jpy, generating a stable signal;
if Jpy is more than Jpy, generating an early warning signal; then executing the step A4;
step A4: comparing the secondary process deviation overlap value Jpe with a secondary deviation threshold Jpe0;
if Jpe is less than or equal to Jpe, generating an intervention signal;
if Jpe > Jpe, generating a therapeutic signal;
and sends the stabilization signal, the intervention signal, the treatment signal, etc. to the linear display module.
9. The motion detection system for ankylosing spondylitis patients according to claim 1, wherein the specific steps of the linear display module are:
acquiring a static data value, a dynamic data value and an action curve data value and combining the virtual coordinate system to display on a display end;
and sending the corresponding detection signals to a display end for display according to the judging result of the analysis judging module.
10. A method for detecting an action in a patient suffering from ankylosing spondylitis, the method being based on the system of any one of claims 1-9, comprising the steps of:
the method comprises the steps that firstly, body position values, maximum body state values and action curve data values of all body positions of a patient under static state are respectively obtained through a body sensor and sent to a data analysis module and a linear display module;
step two: the obtained data are processed through a point processing unit, a linear processing unit and a surface processing unit in the linear fitting module to obtain a deviation value, a displacement value and a dynamic jump value, and the deviation value, the displacement value and the dynamic jump value are sent to the analysis and judgment module;
step three: performing process calculation through the deviation value, the displacement value and the dynamic jump value to obtain a primary process deviation superposition value Jpy and a secondary process deviation superposition value Jpe; the first-level process deviation superposition value Jpy and the second-level process deviation superposition value Jpe are respectively compared with a first-level deviation threshold Jpy and a second-level deviation threshold Jpe0, and detection signals are generated;
step four: and displaying the body position value, the maximum attitude value and the action curve data value and the detection signal through the linear display module.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311411766.4A CN117297593B (en) | 2023-10-30 | 2023-10-30 | Action detection system and detection method for ankylosing spondylitis patient |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311411766.4A CN117297593B (en) | 2023-10-30 | 2023-10-30 | Action detection system and detection method for ankylosing spondylitis patient |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117297593A CN117297593A (en) | 2023-12-29 |
| CN117297593B true CN117297593B (en) | 2024-04-05 |
Family
ID=89246357
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311411766.4A Active CN117297593B (en) | 2023-10-30 | 2023-10-30 | Action detection system and detection method for ankylosing spondylitis patient |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117297593B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004000638A (en) * | 2003-06-02 | 2004-01-08 | Shisei Deetamu:Kk | Blood stream kinetics measuring device, blood stream kinetics measuring method, and recording medium |
| CN106663140A (en) * | 2014-06-30 | 2017-05-10 | 皇家飞利浦有限公司 | Device, system and method for detecting a health condition of a subject |
| CN106778030A (en) * | 2017-01-10 | 2017-05-31 | 广州和康医疗技术有限公司 | A kind of ankylosing spondylitis state of illness monitoring management system and its monitoring management method |
| CN107590708A (en) * | 2016-07-07 | 2018-01-16 | 梁如愿 | A kind of method and apparatus for generating the specific bodily form model of user |
| CN110248601A (en) * | 2016-12-21 | 2019-09-17 | 埃尔瓦有限公司 | Body kinematics or situation are monitored according to motion scheme using conformal electronic device |
| WO2022053080A2 (en) * | 2020-09-10 | 2022-03-17 | 成都拟合未来科技有限公司 | Training method and system based on multi-dimensional movement ability recognition, terminal, and medium |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| NZ572036A (en) * | 2008-10-15 | 2010-03-26 | Nikola Kirilov Kasabov | Data analysis and predictive systems and related methodologies |
-
2023
- 2023-10-30 CN CN202311411766.4A patent/CN117297593B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004000638A (en) * | 2003-06-02 | 2004-01-08 | Shisei Deetamu:Kk | Blood stream kinetics measuring device, blood stream kinetics measuring method, and recording medium |
| CN106663140A (en) * | 2014-06-30 | 2017-05-10 | 皇家飞利浦有限公司 | Device, system and method for detecting a health condition of a subject |
| CN107590708A (en) * | 2016-07-07 | 2018-01-16 | 梁如愿 | A kind of method and apparatus for generating the specific bodily form model of user |
| CN110248601A (en) * | 2016-12-21 | 2019-09-17 | 埃尔瓦有限公司 | Body kinematics or situation are monitored according to motion scheme using conformal electronic device |
| CN106778030A (en) * | 2017-01-10 | 2017-05-31 | 广州和康医疗技术有限公司 | A kind of ankylosing spondylitis state of illness monitoring management system and its monitoring management method |
| WO2022053080A2 (en) * | 2020-09-10 | 2022-03-17 | 成都拟合未来科技有限公司 | Training method and system based on multi-dimensional movement ability recognition, terminal, and medium |
Non-Patent Citations (1)
| Title |
|---|
| 《强直性脊柱炎患者健康效用值映射法应用研究》;杨惠芝;硕士学位论文;20220526;全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117297593A (en) | 2023-12-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP5848762B2 (en) | Real-time quality control of EM calibration | |
| CN101961245B (en) | System and method to compensate for respiratory motion in acquired radiography images | |
| JP4800127B2 (en) | Medical image segmentation device and medical image segmentation program | |
| US8170312B2 (en) | Respiratory motion compensated cardiac wall motion determination system | |
| Tay et al. | In-vivo kinematic analysis of forearm rotation using helical axis analysis | |
| JP4800129B2 (en) | Medical image display processing apparatus and medical image display processing program | |
| CN103608845A (en) | Respiratory motion determination apparatus | |
| WO2010086776A1 (en) | System for providing lung ventilation information | |
| CN102194049A (en) | Diagnosis assisting apparatus and coronary artery analyzing method | |
| Wirth et al. | Comparison of a new inertial sensor based system with an optoelectronic motion capture system for motion analysis of healthy human wrist joints | |
| Hu et al. | Value and clinical application of orthopedic metal artifact reduction algorithm in CT scans after orthopedic metal implantation | |
| JP2006102353A (en) | Apparatus, method and program for analyzing joint motion | |
| Lin et al. | A model‐based tracking method for measuring 3D dynamic joint motion using an alternating biplane x‐ray imaging system | |
| CN1950849A (en) | Pharmacokinetic image registration | |
| Kim et al. | Reliability of coronary artery calcium severity assessment on non-electrocardiogram-gated CT: a meta-analysis | |
| CN117297593B (en) | Action detection system and detection method for ankylosing spondylitis patient | |
| Roy et al. | Automatic analysis method of 3D images in patients with scoliosis by quantifying asymmetry in transverse contours | |
| Montefiori et al. | Variations of lower-limb joint kinematics associated with the use of different ankle joint models | |
| Ksoll et al. | Movement Coordination during functional single-Leg Squat tests in healthy, recreational athletes | |
| McGibbon et al. | Evaluation of anatomical and functional hip joint center methods: the effects of activity type, gender, and proximal reference segment | |
| Binnie et al. | Concurrent validation of inertial sensors for measurement of knee kinematics in individuals with knee osteoarthritis: A technical report | |
| Studholme et al. | Automated multimodality registration using the full affine transformation: Application to MR and CT guided skull base surgery | |
| Kennedy et al. | The effect of cam femoroacetabular impingement on hip maximal dynamic range of motion | |
| Villi et al. | Spine pathologies detections: users’ requirements, technological development and first results. | |
| WO2017179090A1 (en) | Information processing device, information processing method, and information processing program |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |